Automatic Machine Learning
Auto is the new black (2) — AutoGluon, H2O AutoML and Google AutoML Tables
How they work and what’s behind them
While Machine learning keeps achieving considerable successes in solving and improving any kind of problems, an ever-growing number of disciplines rely on it. However, this success crucially relies on machine learning experts to perform manual tasks. AutoML promises to change this reality and transform it into an experience of the type “data in, model out”.
For an introduction to the Automated Machine Learning topic and the description of the methods used by Google AutoML (ENAS), Microsoft Automated ML, AutoKeras and auto-sklearn, visit:
In this post, I will try to explain and help you build some intuition about how AutoGluon, H2O AutoML and Google AutoML Tables work behind the hood (since although all having the word “auto” in their names, they share nothing in common)
AutoGluon (Tabular)
Open-source: Yes
Cloud-based: No, but also available in AWS SageMaker AutoPilot
Supports: Classification, Regression
Techniques: Compilation of pattern and practices common in the ML field…
